Histological Assessment of Tangentially Excised Burn Eschars
Bibliographic record
Abstract
BACKGROUND: The burn eschar serves as a medium for bacterial growth and a source of local and systemic infection. To prevent or minimize these complications, it is important to debride the eschar as early as possible. OBJECTIVE: To identify the presence of viable skin within the excisions by examining tangentially excised burn eschars. METHODS: A total of 146 samples of burned human tissue were removed during 54 routine sharp tangential excision procedures (using dermatomes). The samples were histologically examined to identify the relative thickness of the dead, intermediate and viable layers. RESULTS: The mean (± SD) thickness of the excised samples was 1.7±1.1 mm. The sacrificed viable tissue (mean thickness 0.7±0.8 mm) occupied 41.2% of the entire thickness of the excision. In 32 biopsies (21.8%; 95% CI 16.0 to 29.3), the excision did not reach viable skin. Only eight biopsies (5.4%; 95% CI 2.8 to 10.1) contained all of the necrotic tissue without removing viable tissue. CONCLUSIONS: The thickness of a single tangentially excised layer of eschar is not much greater than the actual thickness of the entire skin and often contains viable tissue. Because surgical debridement is insufficiently selective, more selective means of debriding burn eschars should be explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".